Techniques have been widely used that extract an image feature from an image taken through a lens and detect or classify an object included in an image (hereinafter, referred to as “object detection”). For example, as some of the object detection techniques, techniques that use Local Binary Patterns (hereinafter, referred to as “LBPs”) are described in Patent Literature (hereinafter, referred to as PTL) 1 and Non-Patent Literature (hereinafter, referred to as NPL) 1.
LBP is a binary pattern created by calculating differences in pixel values between each pixel and its surrounding neighborhood pixels and placing the resulting binary numbers. That is, LBPs are information representing gray scale patterns included in an image.
The techniques described in PTL 1 and NPL 1 (hereinafter, referred to as “related art”) determine LBPs of all or a part of pixels within a region in an image targeted for classification (hereinafter, referred to as “target image”). The related art then generates a histogram of values of the LBPs as an image feature. The related art also generates a classifier in advance using histograms generated similarly from images including a predetermined object and images not including the object (hereinafter, collectively referred to as “training images”) and stores the classifier. The related art then evaluates the histogram of the target image using the classifier. The related art thereby determines whether the target image includes the predetermined object.
Histograms of LBPs can represent differences in texture and gray scale patterns more accurately than image features such as histograms of oriented gradients (HOGs). Furthermore, the calculation of histograms of LBPs requires less processing cost compared with HOGs. Thus, the object detection using LBPs, such as the related art, is expected to be applied to various fields.